The present invention relates to popularity prediction of messages in social networks and, particularly, to analysis of social media messages for popularity prediction in social networks.
With the increased use of social media in social networks, prediction of popularity of social media messages has become an interesting task. Popularity detection is useful for risk alarming, online marketing (e.g., recommendation systems, media advertising, etc.) and real-world outcome prediction (e.g., economic trends).
There have been techniques to detect message popularity. Traditional features used in popularity detection tools are normally textual features, author features and sentimental features extracted from messages in social media. The traditional features are analyzed to predict popularity of the messages. There is a problem with using only the traditional features. If the textual feature, the author feature and the sentimental feature of one message are same as or similar to another, the popularity predicted for the one message might be similar to the other, but actually it is not the case. There may even be significant differences in their popularity.